SUMMARY Natural products derived from plants have played crucial roles in the treatments of many diseases throughout history. However, scarce natural sources and complex chemical synthesis have limited research and development and increased costs on these products. An example of this is the non-psychoactive cannabinoids found in cannabis plants. There are over 70 low-abundant cannabinoids that are known to be safe and have beneficial effects ranging from bone growth to neuroprotection. Yet agricultural production of these cannabinoids is not environmentally or economically sustainable. Thus, their therapeutic potentials are unfortunately limited by their availabilities for research and commercial production. Alternative biosynthetic production routes in S. cerevisiae are being investigated to increase accessibility to these high potential therapeutics. Further, massive libraries of yeast mutants are being generated for the purpose of identifying cellular pathways that enhance cannabinoid biosynthesis. However, a major bottleneck in this biosynthetic approach is the lack of high-throughput screening technology that can screen these massive libraries efficiently. Current analytically methods are low-throughput, expensive, cumbersome, and complex. Another problem is that these methods do not enable individual mutant cells to be screened for key metabolite production, which is widely recognized as a limiting problem in metabolic engineering. Single-cell isolation techniques have been explored as high-throughput means to quickly identify and separate metabolically and physiologically favorably mutants for further strain evolution. Therefore, fluorescent sensors that can inform on the intracellular concentrations of valuable metabolites in living cells and enable high-throughput cell sorting would be highly useful tools for any industrial production of natural products. In this application, we will use our fluorescent aptamer technology to develop genetically encoded sensors that can active fluorescence upon binding to target cannabinoid metabolites. These sensors can then be expressed in yeast to monitor cannabinoid production levels in real-time and used in flow cytometry-based screening applications. Taken together, the experiments in this application provide the foundation for a novel approach that can be widely used to improve industrial production of natural products by allowing culture conditions or genetically modified organisms with improved production characteristics to be rapidly identified.